Predicted outputs in a streaming environment

ABSTRACT

According to embodiments of the disclosure, methods, systems, and computer program products for initializing a streaming application are disclosed. The method may include compiling code on a compiler system, the compiling of the code including establishing an operator graph having a plurality of processing elements including a first processing element and a second processing element. The compiling of code including receiving a first compiler directive specifying an operator sub-graph included in the operator graph, the operator sub-graph containing one or more processing elements from the plurality of processing elements. The compiling of code including receiving a second compiler directive identifying the first processing element, the first processing element configured to bypass the operator sub-graph by transmitting a predicted output tuple to the second processing element in response to determining that a processing condition exists in the operator graph.

BACKGROUND

This disclosure generally relates to stream computing, and inparticular, to computing applications that receive streaming data andprocess the data as it is received.

Database systems are typically configured to separate the process ofstoring data from accessing, manipulating, or using data stored in adatabase. More specifically, database systems use a model in which datais first stored and indexed in a memory before subsequent querying andanalysis. In general, database systems may not be well suited forperforming real-time processing and analyzing streaming data. Inparticular, database systems may be unable to store, index, and analyzelarge amounts of streaming data efficiently or in real time.

SUMMARY

Embodiments of the disclosure provide a method, system, and computerprogram product for processing data. The method, system, and computerprogram product receive two or more tuples to be processed by aplurality of processing elements operating on one or more computerprocessors.

A method for initializing a streaming application, the method includingcompiling code on a compiler system, the compiling of the code includingestablishing an operator graph having a plurality of processing elementsincluding a first processing element and a second processing element.The method may include receiving a first compiler directive specifyingan operator sub-graph included in the operator graph. The operatorsub-graph may contain one or more processing elements from the pluralityof processing elements. The operator sub-graph may be configured toreceive an input tuple and configured to transmit an output tuple to thesecond processing element of the operator graph. The method may includereceiving a second compiler directive identifying the first processingelement. The first processing element may be configured to bypass theoperator sub-graph by transmitting a predicted output tuple to thesecond processing element in response to determining that a processingcondition exists in the operator graph. The first processing element maybe configured to bypass the operator sub-graph is in response todetermining that a processing condition exists in the operatorsub-graph.

The method may further include receiving a third compiler directivespecifying the processing condition for the operator graph. Theprocessing condition may include determining that a number of tuples tobe processed in the operator graph is greater than a latency threshold.The processing condition may include determining that a used buffercapacity parameter is greater than a buffer threshold. The processingcondition may include determining the predicted time required to processthe input tuple is greater than a time threshold. The processingcondition may include determining that a CPU usage of the plurality ofprocessing elements is greater than a CPU threshold. The predictedoutput tuple may be selected from one or more predicted output tuplesstored in a prediction table. The predicted output tuple may be selectedbased on historical values of output tuples. The predicted output tuplemay be selected based on a received input tuple.

A computer program product for processing a stream of tuples, thecomputer program product comprising a computer readable storage mediumhaving program code embodied therewith, the program code includecomputer readable program code which may be configured at compile timeto establish an operator graph having a plurality of processing elementsincluding a first processing element and a second processing element.The code may be configured at compile time to transmit a first compilerdirective specifying an operator sub-graph included in the operatorgraph, the operator sub-graph containing one or more processing elementsfrom the plurality of processing elements, the operator sub-graphconfigured to receive an input tuple and configured to transmit anoutput tuple to the second processing element of the operator graph. Thecode may be configured at compile time to transmit a second compilerdirective identifying the first processing element, the first processingelement configured to bypass the operator sub-graph by transmitting apredicted output tuple to the second processing element in response todetermining that a processing condition exists in the operator graph.

The first processing element may be configured to bypass the operatorsub-graph is in response to determining that a processing conditionexists in the operator sub-graph. The code may be further configured totransmit a third compiler directive specifying the processing conditionfor the graph.

A system for processing a stream of tuples may include a plurality ofcompute nodes, the plurality of compute nodes each having one or moreprocessing elements to receive a stream of tuples, the processingelements operating on one or more computer processors, each processingelement having one or more stream operators. The system may include astream manager, the stream manager configured to monitor the pluralityof compute nodes and to determine whether a processing condition existsin the plurality of compute nodes. The system may also include acompiler.

The compiler may be configured to receive a first compiler directivespecifying one or more processing elements of the plurality of computenodes to include in an operator sub-graph. The compiler may beconfigured to receive a second compiler directive identifying a firstprocessing element of the one or more processing elements to bypass theoperator sub-graph by transmitting one or more of the predicted outputtuples to a second processing element of the plurality of compute nodes,in response to determining that a processing condition exists in theplurality of compute nodes. The first processing element may beconfigured to bypass the operator sub-graph is in response todetermining that a processing condition exists in the operatorsub-graph.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a computing infrastructure configured to execute astream computing application according to various embodiments.

FIG. 2 illustrates a more detailed view of a compute node of FIG. 1according to various embodiments.

FIG. 3 illustrates a more detailed view of the management system of FIG.1 according to various embodiments.

FIG. 4 illustrates a more detailed view of the compiler system of FIG. 1according to various embodiments.

FIG. 5A illustrates an operator graph and operator sub-graph for astream computing application according to various embodiments.

FIG. 5B illustrates an operator graph and bypassed operator sub-graphfor a stream computing application according to various embodiments.

FIG. 6 illustrates a flow chart diagram for a method of initializing astreaming application, the method including compiling code on a compilersystem.

Like reference numbers and designations in the various drawings indicatelike elements.

DETAILED DESCRIPTION

Aspects of the present disclosure are generally directed to compilerdirected replacement of one or more output tuples with predicted tuplesin an operator graph of processing elements. While the presentdisclosure is not necessarily limited to such applications, variousaspects of the disclosure may be appreciated through a discussion ofvarious examples using this context.

While the same nomenclature and same numbers may be used to identifyelements throughout the disclosure, this practice is not intended tolimit the scope of the disclosure. Identified elements in one figure maynot be identical to other same named or identified elements in otherfigures.

Stream-based computing and stream-based database computing are emergingas a developing technology for database systems. Products are availablewhich allow users to create applications that process and querystreaming data before it reaches a database file. With this emergingtechnology, users can specify processing logic to apply to inbound datarecords while they are “in flight,” with the results available in a veryshort amount of time, often in fractions of a second. Constructing anapplication using this type of processing has opened up a newprogramming paradigm that will allow for development of a broad varietyof innovative applications, systems, and processes, as well as presentnew challenges for application programmers and database developers.

In a stream computing application, stream operators are connected to oneanother such that data flows from one stream operator to the next (e.g.,over a TCP/IP socket). When a stream operator receives data, it mayperform operations, such as analysis logic, which may change the tupleby adding or subtracting attributes, or updating the values of existingattributes within the tuple. When the analysis logic is complete, a newtuple is then sent to the next stream operator. Scalability is achievedby distributing an application across nodes by creating executables(i.e., processing elements), as well as replicating processing elementson multiple nodes and load balancing among them. Stream operators in astream computing application can be fused together to form a processingelement that is executable. Doing so allows processing elements to sharea common process space, resulting in much faster communication betweenstream operators than is available using inter-process communicationtechniques (e.g., using a TCP/IP socket). Further, processing elementscan be inserted or removed dynamically from an operator graphrepresenting the flow of data through the stream computing application.A particular stream operator may not reside within the same operatingsystem process as other stream operators. In addition, stream operatorsin the same operator graph may be hosted on different nodes, e.g., ondifferent compute nodes or on different cores of a compute node.

Data flows from one stream operator to another in the form of a “tuple.”A tuple is a sequence of one or more attributes associated with anentity. Attributes may be any of a variety of different types, e.g.,integer, float, Boolean, string, etc. The attributes may be ordered. Inaddition to attributes associated with an entity, a tuple may includemetadata, i.e., data about the tuple. A tuple may be extended by addingone or more additional attributes or metadata to it. As used herein,“stream” or “data stream” refers to a sequence of tuples. Generally, astream may be considered a pseudo-infinite sequence of tuples.

Tuples are received and output by stream operators and processingelements. An input tuple corresponding with a particular entity that isreceived by a stream operator or processing element, however, isgenerally not considered to be the same tuple that is output by thestream operator or processing element, even if the output tuplecorresponds with the same entity or data as the input tuple. An outputtuple need not be changed in some way from the input tuple.

Nonetheless, an output tuple may be changed in some way by a streamoperator or processing element. An attribute or metadata may be added,deleted, or modified. For example, a tuple will often have two or moreattributes. A stream operator or processing element may receive thetuple having multiple attributes and output a tuple corresponding withthe input tuple. The stream operator or processing element may onlychange one of the attributes so that all of the attributes of the outputtuple except one are the same as the attributes of the input tuple.

Generally, a particular tuple output by a stream operator or processingelement may not be considered to be the same tuple as a correspondinginput tuple even if the input tuple is not changed by the processingelement. However, to simplify the present description and the claims, anoutput tuple that has the same data attributes or is associated with thesame entity as a corresponding input tuple will be referred to herein asthe same tuple unless the context or an express statement indicatesotherwise.

Stream computing applications may handle massive volumes of data thatneed to be processed efficiently and in real time. For example, a streamcomputing application may continuously ingest and analyze hundreds ofthousands of messages per second and up to petabytes of data per day.Accordingly, each stream operator in a stream computing application maybe required to process a received tuple within fractions of a second.Unless the stream operators are located in the same processing element,it is necessary to use an inter-process communication path each time atuple is sent from one stream operator to another. Inter-processcommunication paths can be a critical resource in a stream computingapplication. According to various embodiments, the available bandwidthon one or more inter-process communication paths may be conserved.Efficient use of inter-process communication bandwidth can speed upprocessing.

Stream operators may transmit a tuple in an operator graph (describedfurther herein). Generally, the operator graph can have a plurality ofstream operators that produce a particular end result, e.g., calculatean average. The operator graph can have a splitting stream operator alsoreferred to as a split operator. The split operator can be a streamoperator that has output ports. Each output port on the split operatorcan route tuples to a plurality of processing branches. The splitoperator can route the tuples to the processing branches using a varietyof routing methods, e.g., randomly, based on the type of processing thata processing branch performs, in intervals, etc.

Each processing branch can have one or more stream operators configuredto perform a particular calculation and produce a different processingresult. For example, if a processing branch is configured to calculatean average calculation. Then the processing branch can have a streamoperator configured to count each attribute value in a tuple, andanother stream operator calculate a total of all attribute values.

Because stream computing applications may deal with large volumes ofdata, the processing of which may be spread over multiple processingelements across multiple compute nodes, a processing element may need toproduce an output faster than it is able. Instead of requiring aprocessing element to generate output data by processing currentlyreceived input data, a processing element may instead output predictiondata, in lieu of the output data, and bypass one or more processingelements. This prediction data (or predicted output data) may be knownprior to the compiling of stream computing applications. The predictedoutput data may be located in a computer storage and accessed by streamcomputing applications to substitute for generated outputs. However, inother embodiments, the predicted output data may be generated based onhistorical values of output data. In an embodiment, the predicted outputdata may also be based on, for example, an average of the output datathat was previously processed and transmitted by the processing element.Using the predicted output data may allow the processing element totransmit output data faster, or with less processing, than it otherwisewould be able.

Moreover, the processing element may output predetermined data only ifthere is a need to limit or stop processing received input data. Forexample, the stream computing application may be experiencingbackpressure. “Backpressure” is a term used to describe one or moreprocessing elements that are unable to transmit or receive additionaldata because either their buffer or a buffer associated with adownstream processing element is full. In the case of some real-timeapplications, the processing element may trade accuracy for increaseddata throughput where the time required for data to propagate throughthe stream computing application is an important factor. It may beadvantageous to allow for predicted output data to substitute forgenerated data in some instances in order to reduce backpressure in astream computing application. Processing conditions, explained furtherbelow, in the operator graph may be identified which signal that theoperator graph is experiencing backpressure.

A method for initializing a streaming application may include compilingcode on a compiler system. The compiling of the code may includeestablishing an operator graph having a plurality of processing elementsincluding a first processing element and a second processing element.The compiling may include receiving a first compiler directivespecifying an operator sub-graph included in the operator graph, theoperator sub-graph containing one or more processing elements from theplurality of processing elements. The operator sub-graph may beconfigured to receive an input tuple and configured to transmit anoutput tuple to the second processing element. The method may alsoinclude receiving a second compiler directive specifying the firstprocessing element, the first processing element modified to bypass theoperator sub-graph by transmitting a predicted output tuple to thesecond processing element in response to determining that a processingcondition exists in the operator graph.

FIG. 1 illustrates one exemplary computing infrastructure 100 that maybe configured to execute a stream computing application, according tosome embodiments. The computing infrastructure 100 includes a managementsystem 105 and two or more compute nodes 110A-110C—i.e., hosts—which arecommunicatively coupled to each other using one or more communicationsnetworks 120. The communications network 120 may include one or moreservers, networks, or databases, and may use a particular communicationprotocol to transfer data between the compute nodes 110A-110C. Acompiler system 102 may be communicatively coupled with the managementsystem 105 and the compute nodes 110 either directly or via thecommunications network 120.

The communications network 120 may include a variety of types ofphysical communication channels or “links.” The links may be wired,wireless, optical, or any other suitable media. In addition, thecommunications network 120 may include a variety of network hardware andsoftware for performing routing, switching, and other functions, such asrouters, switches, or bridges. The communications network 120 may bededicated for use by a stream computing application or shared with otherapplications and users. The communications network 120 may be any size.For example, the communications network 120 may include a single localarea network or a wide area network spanning a large geographical area,such as the Internet. The links may provide different levels ofbandwidth or capacity to transfer data at a particular rate. Thebandwidth that a particular link provides may vary depending on avariety of factors, including the type of communication media andwhether particular network hardware or software is functioning correctlyor at full capacity. In addition, the bandwidth that a particular linkprovides to a stream computing application may vary if the link isshared with other applications and users. The available bandwidth mayvary depending on the load placed on the link by the other applicationsand users. The bandwidth that a particular link provides may also varydepending on a temporal factor, such as time of day, day of week, day ofmonth, or season.

FIG. 2 is a more detailed view of a compute node 110, which may be thesame as one of the compute nodes 110A-110D of FIG. 1, according tovarious embodiments. The compute node 110 may include, withoutlimitation, one or more processors (CPUs) 205, a network interface 215,an interconnect 220, a memory 225, and a storage 230. The compute node110 may also include an I/O device interface 210 used to connect I/Odevices 212, e.g., keyboard, display, and mouse devices, to the computenode 110.

Each CPU 205 retrieves and executes programming instructions stored inthe memory 225 or storage 230. Similarly, the CPU 205 stores andretrieves application data residing in the memory 225. The interconnect220 is used to transmit programming instructions and application databetween each CPU 205, I/O device interface 210, storage 230, networkinterface 215, and memory 225. The interconnect 220 may be one or morebusses. The CPUs 205 may be a single CPU, multiple CPUs, or a single CPUhaving multiple processing cores in various embodiments. In oneembodiment, a processor 205 may be a digital signal processor (DSP). Oneor more processing elements 235 (described below) may be stored in thememory 225. A processing element 235 may include one or more streamoperators 240 (described below). In one embodiment, a processing element235 is assigned to be executed by only one CPU 205, although in otherembodiments the stream operators 240 of a processing element 235 mayinclude one or more threads that are executed on two or more CPUs 205.The memory 225 is generally included to be representative of a randomaccess memory, e.g., Static Random Access Memory (SRAM), Dynamic RandomAccess Memory (DRAM), or Flash. The storage 230 is generally included tobe representative of a non-volatile memory, such as a hard disk drive,solid state device (SSD), or removable memory cards, optical storage,flash memory devices, network attached storage (NAS), or connections tostorage area network (SAN) devices, or other devices that may storenon-volatile data. The network interface 215 is configured to transmitdata via the communications network 120.

A stream computing application may include one or more stream operators240 that may be compiled into a “processing element” container 235. Thememory 225 may include two or more processing elements 235, eachprocessing element having one or more stream operators 240. Each streamoperator 240 may include a portion of code that processes tuples flowinginto a processing element and outputs tuples to other stream operators240 in the same processing element, in other processing elements, or inboth the same and other processing elements in a stream computingapplication. Processing elements 235 may pass tuples to other processingelements that are on the same compute node 110 or on other compute nodesthat are accessible via communications network 120. For example, aprocessing element 235 on compute node 110A may output tuples to aprocessing element 235 on compute node 110B.

The storage 230 may include a buffer 260. The buffer 260 may includestorage space for data flowing into the compute node 110 from upstreamprocessing elements 235 or from a data source for the stream computingapplication. For example, a processing element 235 may include datatuples waiting to be processed by one of the processing elements 235.The buffer 260 may also store output tuples intended for downstreamprocessing elements 235 if the downstream processing element 235 has afull buffer, such as when the operator graph is experiencingbackpressure. Although shown as being in storage, the buffer 260 may belocated in the memory 225 of the compute node 110 or in a combination ofboth memories. Moreover, storage 230 may include storage space that isexternal to the compute node 110, such as in a cloud.

The buffer 260 may also be used to determine when a processing conditionexists in the operator graph. In an embodiment, the processing conditionmay be the existence of backpressure, or latency, in the streamcomputing application. In an embodiment, the processing condition mayinclude determining whether the number of tuples to be processed in anoperator graph is greater than a latency threshold. The substitution ofprediction data 264 for output of an operator sub-graph may decreaselatency and backpressure in the stream computing application. Theprocessing condition may include a capacity use of the data storage ofthe buffer 260 in the compute node 110. For example, the processingcondition may include determining whether a used buffer capacityparameter is greater than a buffer threshold. In an embodiment, the usedbuffer capacity threshold may be 90% used capacity in the buffer so thatif the used buffer capacity parameter is greater than 90% the processingcondition is triggered.

The compute node 110 may include one or more operating systems 262. Anoperating system 262 may be stored partially in memory 225 and partiallyin storage 230. Alternatively, an operating system may be storedentirely in memory 225 or entirely in storage 230. The operating systemprovides an interface between various hardware resources, including theCPU 205, and processing elements and other components of the streamcomputing application. In addition, an operating system provides commonservices for application programs, such as providing a time function.

The compute node 110 may also include prediction data 264. Predictiondata 264 may be stored partially in memory 225 and partially in storage230. Alternatively, prediction data 264 may be stored entirely in memory225 or entirely in storage 230. The prediction data 264 may includeoutput data, such as predicted output tuples, which may be substitutedfor output tuples of an operator graph in event of a processingcondition in a stream computing application. By substituting predictedoutput tuples in lieu of output tuples the operator sub-graph isbypassed. In an embodiment, the processing condition may be theexistence of backpressure, or latency, in the stream computingapplication. In an embodiment, the processing condition may include alatency threshold, where, if a number of tuples to be processed in anoperator graph is greater than the latency threshold, the processingcondition is triggered. The substitution of prediction data 264 maydecrease latency and backpressure in the stream computing application.

The prediction data 264 may be one or more predicted output tupleshaving one or more attributes. In an embodiment, prediction data 264 mayinclude one attribute of an output tuple. In other embodiments,prediction data 264 may include two or more attributes of an outputtuple. In some embodiments, the prediction data 264 may be data which ispre-programmed and arranged in a prediction table and selected aspredicted output tuples by the processing elements 235. One or moreprocessing elements 235 may retrieve prediction data 364 from thestorage 230 and transmit the prediction data 264 downstream in a streamcomputing application. However in other embodiments, the prediction data264 may be generated using historical values of output tuples, describedfurther below. For example, the prediction data 264 may be an average,median, or a mode, of previously computed output tuples for a particulartime period, such as peak usage hours, or output tuples that correspondto a particular input tuple received by the processing element 235. Inan embodiment, the prediction data may be generated by the streammanager 134 (FIG. 1). In another embodiment, the prediction data may begenerated by the processing elements 235. In other embodiments theprediction data may be generated by a predictive system, such as SPSSsoftware, external to the computing infrastructure 100.

FIG. 3 is a more detailed view of the management system 105 of FIG. 1according to some embodiments. The management system 105 may include,without limitation, one or more processors (CPUs) 305, a networkinterface 315, an interconnect 320, a memory 325, and a storage 330. Themanagement system 105 may also include an I/O device interface 310connecting I/O devices 312, e.g., keyboard, display, and mouse devices,to the management system 105.

Each CPU 305 retrieves and executes programming instructions stored inthe memory 325 or storage 330. Similarly, each CPU 305 stores andretrieves application data residing in the memory 325 or storage 330.The interconnect 320 is used to move data, such as programminginstructions and application data, between the CPU 305, I/O deviceinterface 310, storage unit 330, network interface 315, and memory 325.The interconnect 320 may be one or more busses. The CPUs 305 may be asingle CPU, multiple CPUs, or a single CPU having multiple processingcores in various embodiments. In one embodiment, a processor 305 may bea DSP. Memory 325 is generally included to be representative of a randomaccess memory, e.g., SRAM, DRAM, or Flash. The storage 330 is generallyincluded to be representative of a non-volatile memory, such as a harddisk drive, solid state device (SSD), removable memory cards, opticalstorage, Flash memory devices, network attached storage (NAS),connections to storage area-network (SAN) devices, or the cloud. Thenetwork interface 315 is configured to transmit data via thecommunications network 120.

The memory 325 may store a stream manager 134. Additionally, the storage330 may store an operator graph 335. The operator graph 335 may definehow tuples are routed to processing elements 235 (FIG. 2) forprocessing.

The stream manager 134 may include a predicted data module 337 which mayinclude logic for accessing prediction data 264 (FIG. 2) for aprocessing element 235. The stream manager 134 may monitor backpressurein the operator graph and may determine whether latency needs to beimproved. For example, a processing element 235 which is unable toprocess tuples as fast as they are received may cause unprocessed tuplesto fill up the buffer. The stream manager 134 may direct a firstprocessing element of the processing elements 235 to cease processing ofsome percentage of the tuples and direct the first processing element oranother processing element 235 to transmit prediction data 264 insubstitution for typical output tuples. The unprocessed tuples may thenbe discarded.

In an embodiment, the stream manager 134 may determine whether aprocessing condition exists in the operator graph. The processingcondition may be various conditions involving the stream computingapplication. In an embodiment, the processing condition signalsbackpressure or latency in the operator graph. The processing conditionmay be based on various information about the operator graph. In anembodiment the processing condition may include determining whether thenumber of tuples to be processed in the operator graph is greater than alatency threshold. In an embodiment, the latency threshold may be500,000 queued tuples in the operator graph so that if the number oftuples to be processed is greater than 500,000 queued tuples, theprocessing condition is triggered. In an embodiment, the latencythreshold may be 500,000 queued tuples in the operator sub-graph graphso that if the number of tuples to be processed is greater than 500,000queued tuples, the processing condition is triggered. In anotherembodiment the processing condition may be determining whether the usedbuffer capacity parameter in a compute node 110 is greater than a bufferthreshold. In an embodiment, the buffer threshold may be 90% usedcapacity in the buffer so that if the used buffer capacity parameter isgreater than 90% the processing condition is triggered. However, othertypes of processing conditions may be used.

The predicted data module 337 may also include logic used by the streammanager 134 to generate prediction data 264 for the processing elements235. The prediction data 264 may be generated from various types ofhistorical values of tuples in the operator graph. The prediction data264 may be the result of a statistical operation. In an embodiment, thepredicted data module 337 may compute the average for a plurality ofpreviously transmitted output values or determine the mode of the outputvalues. The predicted data module 337 may continually generateprediction data 264 for each processing element 235 in an operatorgraph, or generate predicted output data only after the stream manager134 has determined that a processing condition exists in the operatorgraph 335. When the processing condition is no longer true, the streammanager may direct the first processing element of the processingelements 235 to resume processing of some percentage of the tuples anddirect the first processing element or another processing element 235 tooutput typical output tuples.

The management system 105 may include one or more operating systems 332.An operating system 332 may be stored partially in memory 325 andpartially in storage 330. Alternatively, an operating system may bestored entirely in memory 325 or entirely in storage 330. The operatingsystem provides an interface between various hardware resources,including the CPU 305, and processing elements and other components ofthe stream computing application. In addition, an operating systemprovides common services for application programs, such as providing atime function.

FIG. 4 is a more detailed view of the compiler system 102 of FIG. 1according to some embodiments. The compiler system 102 may include,without limitation, one or more processors (CPUs) 405, a networkinterface 415, an interconnect 420, a memory 425, and storage 430. Thecompiler system 102 may also include an I/O device interface 410connecting I/O devices 412, e.g., keyboard, display, and mouse devices,to the compiler system 102.

Each CPU 405 retrieves and executes programming instructions stored inthe memory 425 or storage 430. Similarly, each CPU 405 stores andretrieves application data residing in the memory 425 or storage 430.The interconnect 420 is used to move data, such as programminginstructions and application data, between the CPU 405, I/O deviceinterface 410, storage unit 430, network interface 415, and memory 425.The interconnect 420 may be one or more busses. The CPUs 405 may be asingle CPU, multiple CPUs, or a single CPU having multiple processingcores in various embodiments. In one embodiment, a processor 405 may bea DSP. Memory 425 is generally included to be representative of a randomaccess memory, e.g., SRAM, DRAM, or Flash. The storage 430 is generallyincluded to be representative of a non-volatile memory, such as a harddisk drive, solid state device (SSD), removable memory cards, opticalstorage, flash memory devices, network attached storage (NAS),connections to storage area-network (SAN) devices, or to the cloud. Thenetwork interface 415 is configured to transmit data via thecommunications network 120.

The compiler system 102 may include one or more operating systems 432.An operating system 432 may be stored partially in memory 425 andpartially in storage 430. Alternatively, an operating system may bestored entirely in memory 425 or entirely in storage 430. The operatingsystem provides an interface between various hardware resources,including the CPU 405, and processing elements and other components ofthe stream computing application. In addition, an operating systemprovides common services for application programs, such as providing atime function.

The memory 425 may store a compiler 136. The compiler 136 compilesmodules, which include source code or statements, into the object code,which includes machine instructions that execute on a processor. In oneembodiment, the compiler 136 may translate the modules into anintermediate form before translating the intermediate form into objectcode. The compiler 136 may output a set of deployable artifacts that mayinclude a set of processing elements and an application descriptionlanguage file (ADL file), which is a configuration file that describesthe stream computing application. In some embodiments, the compiler 136may be a just-in-time compiler that executes as part of an interpreter.In other embodiments, the compiler 136 may be an optimizing compiler. Invarious embodiments, the compiler 136 may perform peepholeoptimizations, local optimizations, loop optimizations, inter-proceduralor whole-program optimizations, machine code optimizations, or any otheroptimizations that reduce the amount of time required to execute theobject code, to reduce the amount of memory required to execute theobject code, or both.

The output of the compiler 136 may be represented by an operator graph,e.g., the operator graph 335. The complier 136 may compile a code forinitializing a streaming application, the compiling of the code mayinclude establishing an operator graph of a plurality of processingelements including a first processing element and a second processingelement. The operator graph may include an operator sub-graph containingone or more processing elements from the plurality of stream operators.The operator sub-graph may be configured to receive an input tuple froma processing element of the operator graph and configured to transmit anoutput tuple to the second processing element. The compiling of codealso including modifying the first processing element to bypass theoperator sub-graph by transmitting a predicted output tuple to thesecond processing element in response to determining that a processingcondition exists in the plurality of processing elements. The firstprocessing element may be located out of the operator sub-graph. Inother embodiments, the first processing element may be located in theoperator sub-graph and may bypass the operator sub-graph. Bypassing theoperator sub-graph means substituting the output tuple of the operatorsub-graph with the predicted output tuple from the first processingelement.

The complier 136 may be configured to receive one or more compilerdirectives from a developer. The compiler directives may instruct thecompiler 136 on how the compiler 136 should process the code. Thecomplier 136 may be configured to receive a first compiler directivewhich may specify the operator sub-graph. The first compiler directivemay cause the compiler to add a tag to compiled code associated with theoperator sub-graph. The operator sub-graph may include one or moreprocessing elements 235 (FIG. 2) from the operator graph. The compiler136 may also be configured to receive a second compiler directivespecifying a first processing element to bypass the operator sub-graphby transmitting a predicted output tuple to the second processingelement in response to determining that a processing condition exists inthe operator graph. The second compiler directive may cause the compilerto add instructions to compiled code associated with the firstprocessing element. The compiler may also be configured to receive athird compiler directive which may specific a processing condition forthe operator graph. The third compiler directive may cause the compilerto add instructions and the processing condition to compiled codeassociated with the stream manager or with a processing element. Theprocessing condition may be the same or substantially similar asdescribed herein. In an embodiment, the second compiler directive mayspecify a processing condition for the operator sub-graph or otherportion of the operator graph.

The compiler 136 may also provide the application administrator with theability to optimize performance through profile-driven fusionoptimization. Fusing operators may improve performance by reducing thenumber of calls to a transport. While fusing stream operators mayprovide faster communication between operators than is available usinginter-process communication techniques, any decision to fuse operatorsrequires balancing the benefits of distributing processing acrossmultiple compute nodes with the benefit of faster inter-operatorcommunications. The compiler 136 may automate the fusion process todetermine how to best fuse the operators to be hosted by one or moreprocessing elements, while respecting user-specified constraints. Thismay be a two-step process, including compiling the application in aprofiling mode and running the application, then re-compiling and usingthe optimizer during this subsequent compilation. The end result may,however, be a compiler-supplied deployable application with an optimizedapplication configuration.

FIGS. 5A and 5B illustrate an operator graph 500 for a stream computingapplication beginning from source 502 through to sink 506, according tosome embodiments. This flow from source to sink may also be generallyreferred to herein as an execution path. In addition, a flow from oneprocessing element to another may be referred to as an execution path invarious contexts. Although FIG. 5 is abstracted to show connectedprocessing elements PE1-PE6, the operator graph 500 may include dataflows between stream operators 240 (FIG. 2) within the same or differentprocessing elements. Typically, processing elements, such as processingelement 235 (FIG. 2), receive tuples from the stream as well as outputtuples into the stream (except for a sink—where the stream terminates,or a source—where the stream begins). While the operator graph 500includes a relatively small number of components, an operator graph maybe much more complex and may include many individual operator graphsthat may be statically or dynamically linked together.

The example operator graph shown in FIGS. 5A and 5B include sixprocessing elements (labeled as PE1-PE6) running on the compute nodes110A-110C. A processing element may include one or more stream operatorsfused together to form an independently running process with its ownprocess ID (PID) and memory space. In cases where two (or more)processing elements are running independently, inter-processcommunication may occur using a “transport,” e.g., a network socket, aTCP/IP socket, or shared memory. Inter-process communication paths usedfor inter-process communications can be a critical resource in a streamcomputing application. However, when stream operators are fusedtogether, the fused stream operators can use more rapid communicationtechniques for passing tuples among stream operators in each processingelement.

The operator graph 500 begins at a source 502 and ends at a sink 506.Compute node 110A includes the processing elements PE1 and PE2. Source502 flows into the processing element PE1, which in turn outputs tuplesthat are received by PE2. For example, PE1 may split pass dataattributes received in a tuple unchanged to PE2. In another example, PE1may change value of one or more attributes and pass some data attributesunchanged in a new tuple to PE2. In another example, one or moreattributes may be added to the tuple or dropped from the tuple and thenpassed to PE2. In another example, a new attribute based on PE1'sprocessing may be added to the tuple. Tuples that flow to PE2 areprocessed by the stream operators contained in PE2. The resulting tuplesmay then be outputted to PE3 on compute node 110B as shown in FIG. 5A.In FIG. 5A, the tuples entering compute node 110B may flow to PE3 andthrough to PE4. PE4 may output tuples to compute node 110C. Tuplesentering compute node 110C flow to PE5 through to PE6 and to operatorsink 506.

However, the processing element PE2 may be specified to bypass computenode 110B as shown in FIG. 5B. In FIG. 5B predicted output tuples may beoutputted to PE5 on compute node 110C. The predicted output tuples maybe contained in the compute nodes 110A, 110B, 110C. In an embodiment,the predicted output tuples may be outputted from PE2 in compute node110A. In another embodiment the predicted output tuples may be outputtedfrom PE4 in compute node 110B. In another embodiment, PE5 may selectpredicted output tuples as inputs in compute node 110C. The bypassing ofcompute node 110B may be based on the existence of a processingcondition as discussed further below. Tuples entering compute node 110Cflow to PE5 through to PE6 and to operator sink 506.

Various portions of the operator graph 500 may be in operator sub-graph501. In an embodiment, the operator sub-graph 501 may include computenode 110B. However, in other embodiments the operator sub-graph 501 mayinclude one or more processing elements of the operator graph 500. Theoperator sub-graph may be created by the compiler in the compiling ofcode as described above. In an embodiment, the compiler may receive afirst compiler directive which may specify the one or more processingelements in the operator sub-graph. Compute node 110A may include afirst processing element PE2 which may be configured to bypass theoperator sub-graph in response to determining that the processingcondition exists in the operator graph. Whether the processing conditionexists in the operator graph may be determined by the stream monitor,described herein, by the processing elements of the operator graph, orother suitable element. In other embodiments, the processing conditionmay be determined by processing elements, e.g. PE2 or PE3.

The processing condition may be various conditions involving the streamcomputing application. In an embodiment, the processing condition may bedetermining that a number of tuples to be processed in the operatorgraph is greater than a latency threshold. In some embodiments, theprocessing condition may be based on the number of tuples to beprocessed in the operator sub-graph or other area in the operator graph.In another embodiment, the processing condition may be based onprocessing elements which are upstream from the operator sub-graph.

In an embodiment the latency threshold may be selected as 500,000 tuplesto be processed by the operator sub-graph so that if the number oftuples to be processed is greater than 500,000 queued tuples, theprocessing condition is triggered. In another embodiment the processingcondition may be determining whether a used buffer capacity parameter isgreater than a buffer threshold. In an embodiment, the buffer thresholdmay be 90% used capacity in the buffer so that if the used buffercapacity parameter is greater than 90% the processing condition istriggered. In other embodiments, the processing condition may bedetermining whether the predicted time required to process the inputtuple is greater than a time threshold. In another embodiment, theprocessing condition may be determining that a CPU usage of theplurality of processing elements is greater than a CPU threshold.

As described above, the stream manager 134 (FIG. 1) may determine whenthe processing condition is triggered. The stream manager 134 may beconfigured to monitor a stream computing application running on thecompute nodes, e.g., compute nodes 110A-110C, as well as to change thedeployment of an operator graph, e.g., operator graph 132. The streammanager 134 may move processing elements from one compute node 110 toanother, for example, to manage the processing loads of the computenodes 110A-110C in the computing infrastructure 100. Further, streammanager 134 may control the stream computing application by inserting,removing, fusing, un-fusing, or otherwise modifying the processingelements and stream operators (or what tuples flow to the processingelements) running on the compute nodes 110A-110C. In an embodiment, whenthe processing condition is detected by the stream manager 134 thestream manager may direct the flow of tuples to bypass the operatorsub-graph 501 by sending prediction data 264 from the processingelements 235 to PE5 in lieu of normal output tuples from the operatorsub-graph.

However, in other embodiments, the compute node 110 or the processingelements 235 may also determine when the processing condition istriggered e.g., PE2 or PE3. In some embodiments, when the processingcondition is triggered, the processing elements of compute node 110 maysubstitute prediction data such as predicted output tuples to bypass theoperator sub-graph 501. The computer node may include prediction data264 and the processing elements of the compute node 110 may have accessto the prediction data. In FIG. 5 Processing element PE5 may receivepredicted output tuples from the processing elements of compute node110A when the processing condition is triggered. Thus, the streamcomputing application may increase data throughput and reduce latency inthe stream computing application.

Processing elements 235 (FIG. 2) may be configured to receive or outputtuples in various formats, e.g., the processing elements or streamoperators could exchange data marked up as XML documents. Furthermore,each stream operator 240 within a processing element 235 may beconfigured to carry out any form of data processing functions onreceived tuples, including, for example, writing to database tables orperforming other database operations such as data joins, splits, reads,etc., as well as performing other data analytic functions or operations.

Because a processing element may be a collection of fused streamoperators, it is equally correct to describe the operator graph as oneor more execution paths between specific stream operators, which mayinclude execution paths to different stream operators within the sameprocessing element. FIG. 5 illustrates execution paths betweenprocessing elements for the sake of clarity.

Referring now to FIG. 6 a flow chart diagram of a method forinitializing a streaming application, the method including compilingcode on a compiler system, according to an embodiment. In operation 602,source code may be received. The source code may include executablestatements such as machine instructions that execute on a processor. Asdescribed above, the source code may be compiled in a compiler 136 (FIG.1).

In operation 604, compiler directives may be received. The complier 136(FIG. 1) may be configured to receive one or more compiler directivesfrom a developer. The compiler directives may instruct the compiler 136on how the compiler 136 should process the source code. The complier 136may be configured to receive a first compiler directive which mayspecify an operator sub-graph. The operator sub-graph may be a subset ofthe operator graph and may include one or more processing elements ofthe operator graph. The first compiler directive may cause the compilerto add a tag to compiled code associated with the operator sub-graph.The operator sub-graph may include one or more processing elements 235(FIG. 2) from the operator graph.

The compiler 136 may also be configured to receive a second compilerdirective specifying a first processing element to bypass the operatorsub-graph by transmitting a predicted output tuple to the secondprocessing element in response to determining that a processingcondition exists in the operator graph. The second compiler directivemay cause the compiler to add instructions to compiled code associatedwith the first processing element. The compiler may also be configuredto receive a third compiler directive which may specific a processingcondition for the operator graph. The third compiler directive may causethe compiler to add instructions and the processing condition tocompiled code associated with the stream manager or with a processingelement. The processing condition may be the same or substantiallysimilar as described herein. In an embodiment, the second compilerdirective may specify a processing condition for the operator sub-graphor other portion of the operator graph. Commonly, the compilerdirectives may be provided in the source code itself via a tag in thecode.

In operation 606, the source code may be compiled into object codedefining the operator graph. As described above, the operator graph mayrepresent the flow of data through the stream computing application andmay define how tuples are routed to processing elements 235 (FIG. 2) forprocessing. The operator graph may include one or more processingelements. The one or more processing elements may include one or morestream operators. The operator graph may include at least a firstprocessing element and a second processing element. However, theoperator graph may include a plurality of processing elements, asdescribed above. The source code may also be compiled into object codedefining an operator sub-graph. Various portions of the operator graphmay be an operator sub-graph. The operator sub-graph may include one ormore processing elements of the operator graph. The operator sub-graphmay be created by the compiler in the compiling of code as describedabove. The operator sub-graph may be configured to receive an inputtuple from a processing element of the operator graph and transmit anoutput tuple to the second processing element. In operation 608, sourcecode may be executed.

In operation 610, an input tuple may be received at a first processingelement. The input tuple may be received from a stream source or as anoutput from another processing element upstream from the firstprocessing element.

If a processing condition is present in the streaming application then,in decision block 612, the method 600 may progress to operation 614. Thefirst processing element may be configured to transmit predicted outputdata when the processing condition is detected in the operatorsub-graph. The first processing element may be identified at compiletime, as the compiler may be configured to receive a second compilerdirective specifying a first processing element to bypass the operatorsub-graph. The first processing element may be located out of theoperator sub-graph. In other embodiments, the first processing elementmay be located in the operator sub-graph and may bypass the operatorsub-graph. Bypassing the operator sub-graph means substituting theoutput tuple of the operator sub-graph with the predicted output tuplefrom the first processing element.

The processing condition may be detected by the stream manager, asdescribed above or by other suitable element in the operator graph. Theprocessing condition may be various conditions involving the streamcomputing application. In an embodiment, the processing condition may bedetermining that a number of tuples to be processed in the operatorgraph is greater than a latency threshold. In an embodiment the latencythreshold may be 500,000 queued tuples so that if the number of tuplesto be processed is greater than 500,000 queued tuples, the processingcondition is triggered. However, other processing conditions may be useddepending upon the preferences of the steam computing application. Theprocessing condition may be identified at compile time. The compiler mayalso be configured to receive a third compiler directive which mayspecific a processing condition for the operator graph. The processingcondition may be the same or substantially similar as described herein.In an embodiment, the second compiler directive may specify a processingcondition for the operator sub-graph or other portion of the operatorgraph.

In operation 614, the operator sub-graph may be bypassed. The operatorsub-graph may be bypassed by modifying the first processing element tobypass the operator sub-graph and transmit tuples a second processingelement, described above. The first processing element may be locatedout of the operator sub-graph. In other embodiments, the firstprocessing element may be located in the operator sub-graph and maybypass the operator sub-graph. Bypassing the operator sub-graph meanssubstituting the output tuple of the operator sub-graph with thepredicted output tuple from the first processing element. In response toreceiving tuples the first processing element may look to the predictiondata, as described above, and transmit a predicted output tuple storedin prediction data in lieu of generated output tuples from the operatorsub-graph. Thus, a predicted output tuple may be received at a secondprocessing element.

If the processing condition is not present then, in decision block 612,the method 600 may progress to operation 616. In operation 616 the firstprocessing element may transmit tuples along the operator sub-graph. Ifno processing condition exists, such as queued tuples, then the operatorgraph may not have latency and the method may not require the operatorgraph to sacrifice accuracy for improved throughput. Therefore, theoperator graph may transmit tuples using the operator sub-graph and maygenerate output tuples instead of substituting predicted results.

In the foregoing, reference is made to various embodiments. It should beunderstood, however, that this disclosure is not limited to thespecifically described embodiments. Instead, any combination of thedescribed features and elements, whether related to differentembodiments or not, is contemplated to implement and practice thisdisclosure. Furthermore, although embodiments of this disclosure mayachieve advantages over other possible solutions or over the prior art,whether or not a particular advantage is achieved by a given embodimentis not limiting of this disclosure. Thus, the described aspects,features, embodiments, and advantages are merely illustrative and arenot considered elements or limitations of the appended claims exceptwhere explicitly recited in a claim(s).

Aspects of the present disclosure may be embodied as a system, method,or computer program product. Accordingly, aspects of the presentdisclosure may take the form of an entirely hardware embodiment, anentirely software embodiment (including firmware, resident software,micro-code, etc.), or an embodiment combining software and hardwareaspects that may all generally be referred to herein as a “circuit,”“module,” or “system.” Furthermore, aspects of the present disclosuremay take the form of a computer program product embodied in one or morecomputer readable medium(s) having computer readable program codeembodied thereon.

Any combination of one or more computer readable medium(s) may beutilized. The computer readable medium may be a computer readable signalmedium or a computer readable storage medium. A computer readablestorage medium may be, for example, but not limited to, an electronic,magnetic, optical, electromagnetic, infrared, or semiconductor system,apparatus, or device, or any suitable combination thereof. More specificexamples (a non-exhaustive list) of the computer readable storage mediumwould include the following: an electrical connection having one or morewires, a portable computer diskette, a hard disk, a random access memory(RAM), a read-only memory (ROM), an erasable programmable read-onlymemory (EPROM or Flash memory), an optical fiber, a portable compactdisc read-only memory (CD-ROM), an optical storage device, a magneticstorage device, or any suitable combination thereof. In the context ofthis disclosure, a computer readable storage medium may be any tangiblemedium that can contain, or store, a program for use by or in connectionwith an instruction execution system, apparatus, or device.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including, but not limited to,electro-magnetic, optical, or any suitable combination thereof. Acomputer readable signal medium may be any computer readable medium thatis not a computer readable storage medium and that can communicate,propagate, or transport a program for use by or in connection with aninstruction execution system, apparatus, or device.

Program code embodied on a computer readable medium may be transmittedusing any appropriate medium, including but not limited to wireless,wire line, optical fiber cable, RF, etc., or any suitable combinationthereof.

Computer program code for carrying out operations for aspects of thepresent disclosure may be written in any combination of one or moreprogramming languages, including: (a) an object oriented programminglanguage; (b) conventional procedural programming languages; and (c) astreams programming language, such as IBM Streams Processing Language(SPL). The program code may execute as specifically described herein. Inaddition, the program code may execute entirely on the user's computer,partly on the user's computer, as a stand-alone software package, partlyon the user's computer and partly on a remote computer, or entirely onthe remote computer or server. In the latter scenario, the remotecomputer may be connected to the user's computer through any type ofnetwork, including a local area network (LAN) or a wide area network(WAN), or the connection may be made to an external computer (forexample, through the Internet using an Internet Service Provider).

Aspects of the present disclosure have been described with reference toflowchart illustrations, block diagrams, or both, of methods,apparatuses (systems), and computer program products according toembodiments of this disclosure. It will be understood that each block ofthe flowchart illustrations or block diagrams, and combinations ofblocks in the flowchart illustrations or block diagrams, can beimplemented by computer program instructions. These computer programinstructions may be provided to a processor of a general purposecomputer, special purpose computer, or other programmable dataprocessing apparatus to produce a machine, such that the instructions,which execute via the processor of the computer or other programmabledata processing apparatus, create means for implementing the functionsor acts specified in the flowchart or block diagram block or blocks.

These computer program instructions may also be stored in a computerreadable medium that can direct a computer, other programmable dataprocessing apparatus, or other devices to function in a particularmanner, such that the instructions stored in the computer readablemedium produce an article of manufacture including instructions whichimplement the function or act specified in the flowchart or blockdiagram block or blocks.

The computer program instructions may also be loaded onto a computer,other programmable data processing apparatus, or other devices to causea series of operational steps to be performed on the computer, otherprogrammable apparatus, or other devices to produce a computerimplemented process such that the instructions which execute on thecomputer or other programmable apparatus provide processes forimplementing the functions or acts specified in the flowchart or blockdiagram block or blocks.

Embodiments according to this disclosure may be provided to end-usersthrough a cloud-computing infrastructure. Cloud computing generallyrefers to the provision of scalable computing resources as a serviceover a network. More formally, cloud computing may be defined as acomputing capability that provides an abstraction between the computingresource and its underlying technical architecture (e.g., servers,storage, networks), enabling convenient, on-demand network access to ashared pool of configurable computing resources that can be rapidlyprovisioned and released with minimal management effort or serviceprovider interaction. Thus, cloud computing allows a user to accessvirtual computing resources (e.g., storage, data, applications, and evencomplete virtualized computing systems) in “the cloud,” without regardfor the underlying physical systems (or locations of those systems) usedto provide the computing resources.

Typically, cloud-computing resources are provided to a user on apay-per-use basis, where users are charged only for the computingresources actually used (e.g., an amount of storage space used by a useror a number of virtualized systems instantiated by the user). A user canaccess any of the resources that reside in the cloud at any time, andfrom anywhere across the Internet. In context of the present disclosure,a user may access applications or related data available in the cloud.For example, the nodes used to create a stream computing application maybe virtual machines hosted by a cloud service provider. Doing so allowsa user to access this information from any computing system attached toa network connected to the cloud (e.g., the Internet).

The flowchart and block diagrams in the figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present disclosure. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof code, which comprises one or more executable instructions forimplementing the specified logical function(s). It should also be notedthat, in some alternative implementations, the functions noted in theblock may occur out of the order noted in the figures. For example, twoblocks shown in succession may, in fact, be executed substantiallyconcurrently, or the blocks may sometimes be executed in the reverseorder, depending upon the functionality involved. It will also be notedthat each block of the block diagrams or flowchart illustration, andcombinations of blocks in the block diagrams or flowchart illustration,can be implemented by special purpose hardware-based systems thatperform the specified functions or acts, or combinations of specialpurpose hardware and computer instructions.

Although embodiments are described within the context of a streamcomputing application, this is not the only context relevant to thepresent disclosure. Instead, such a description is without limitationand is for illustrative purposes only. Additional embodiments may beconfigured to operate with any computer system or application capable ofperforming the functions described herein. For example, embodiments maybe configured to operate in a clustered environment with a standarddatabase processing application. A multi-nodal environment may operatein a manner that effectively processes a stream of tuples. For example,some embodiments may include a large database system, and a query of thedatabase system may return results in a manner similar to a stream ofdata.

While the foregoing is directed to exemplary embodiments, other andfurther embodiments of the disclosure may be devised without departingfrom the basic scope thereof, and the scope thereof is determined by theclaims that follow.

1. A method for initializing a streaming application, the methodincluding compiling code on a compiler system, the compiling of the codecomprising: establishing an operator graph having a plurality ofprocessing elements including a first processing element and a secondprocessing element; receiving a first compiler directive specifying anoperator sub-graph included in the operator graph, the operatorsub-graph containing one or more processing elements from the pluralityof processing elements, the operator sub-graph configured to receive aninput tuple and configured to transmit an output tuple to the secondprocessing element of the operator graph; and receiving a secondcompiler directive identifying the first processing element, the firstprocessing element configured to bypass the operator sub-graph bytransmitting a predicted output tuple to the second processing elementin response to determining that a processing condition exists in theoperator graph.
 2. The method of claim 1, wherein the first processingelement is configured to bypass the operator sub-graph is in response todetermining that a processing condition exists in the operatorsub-graph.
 3. The method of claim 1, further comprising receiving athird compiler directive specifying the processing condition for thegraph.
 4. The method of claim 1, wherein the processing condition isdetermining that a number of tuples to be processed in the operatorgraph is greater than a latency threshold.
 5. The method of claim 1,wherein the processing condition is determining that a used buffercapacity parameter is greater than a buffer threshold.
 6. The method ofclaim 1, wherein the processing condition is determining the predictedtime required to process the input tuple is greater than a timethreshold.
 7. The method of claim 1, wherein the processing condition isdetermining that a CPU usage of the plurality of processing elements isgreater than a CPU threshold.
 8. The method of claim 1, wherein thepredicted output tuple is selected from one or more predicted outputtuples stored in a prediction table.
 9. The method of claim 1, whereinthe predicted output tuple is selected based on historical values ofoutput tuples.
 10. The method of claim 1, wherein the predicted outputtuple is selected based on a received input tuple.
 11. A computerprogram product for processing a stream of tuples, the computer programproduct comprising a computer readable storage medium having programcode embodied therewith, the program code comprising computer readableprogram code configured at compile time to: establish an operator graphhaving a plurality of processing elements including a first processingelement and a second processing element; transmit a first compilerdirective specifying an operator sub-graph included in the operatorgraph, the operator sub-graph containing one or more processing elementsfrom the plurality of processing elements, the operator sub-graphconfigured to receive an input tuple and configured to transmit anoutput tuple to the second processing element of the operator graph; andtransmit a second compiler directive identifying the first processingelement, the first processing element configured to bypass the operatorsub-graph by transmitting a predicted output tuple to the secondprocessing element in response to determining that a processingcondition exists in the operator graph.
 12. The computer program productof claim 11, wherein the first processing element is configured tobypass the operator sub-graph is in response to determining that aprocessing condition exists in the operator sub-graph.
 13. The computerprogram product of claim 11, wherein the code is further configured totransmit a third compiler directive specifying the processing conditionfor the graph.
 14. The computer program product of claim 11, wherein theprocessing condition is determining that a number of tuples to beprocessed in the operator graph is greater than a latency threshold. 15.The computer program product of claim 11, wherein the processingcondition is determining that a used buffer capacity parameter isgreater than a buffer threshold.
 16. The computer program product ofclaim 11, wherein the predicted output tuple is selected based onhistorical values of output tuples. 17-20. (canceled)